Math The Delightful Consequences of the Graph Minor Theorem The graph minor theorem, also known as the Robertson–Seymour theorem, is generally regarded as the most important result in graph theory. In this post we introduce the graph minor theorem, provide the necessary
Code Universal types, and your type checker doesn't suck as much as you think Universal types are very useful for performing generic programming, which allows you to use the same code over different types. For instance, the C++ STL (Standard Template Library) allows you to work on
General Against Government Scholarships Over the past few years, I’ve received numerous questions about studying abroad and whether it is advisable to take a government scholarship to do so from my juniors. Having witnessed firsthand the early
Code Why you can't create a value with the Bottom type (and why it's still useful) If you’ve used any sort of object-oriented language, you may be familiar with the notion but not the name of the top-level type \(\top\). For instance, in Java, the Object class forms the
Code Quantum physics inaccuracies in Seishun Buta Yaro (Rascal Does Not Dream of Bunny Girl Senpai) I finished watching Seishun Buta Yaro (Rascal Does Not Dream of Bunny Girl Senpai) recently, and really enjoyed the anime. It explored themes of finding one’s identity, and dealing with social anxiety during
Code Writing a DPLL SAT Solver Boolean satisfiability (SAT) solvers have played an important role in software and hardware verification, automatic test pattern generation, planning, scheduling, and solving challenging problems in algebra.
Machine learning Playing Atari using Deep Reinforcement Learning In this post, we study the first deep reinforcement learning model that was successfully able to learn control policies directly from high dimensional sensory inputs, as applied to games on the Atari platform
Machine learning HopSkipJumpAttack: An Efficient Adversarial Attack against Machine Learning Algorithms Many machine learning algorithms have been shown to be susceptible to adversarial examples. For example, image classification neural networks can wrongly classify an image when a small perturbation, unnoticeable to the human eye,